Guangxi Academy of science

Nanning, China

Guangxi Academy of science

Nanning, China

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Xie N.-Z.,Guangxi Academy of science | Liang H.,CAS Institute of Microbiology | Huang R.-B.,Guangxi Academy of science | Xu P.,Shanghai JiaoTong University
Biotechnology Advances | Year: 2014

Muconic acid (MA), a high value-added bio-product with reactive dicarboxylic groups and conjugated double bonds, has garnered increasing interest owing to its potential applications in the manufacture of new functional resins, bio-plastics, food additives, agrochemicals, and pharmaceuticals. At the very least, MA can be used to produce commercially important bulk chemicals such as adipic acid, terephthalic acid and trimellitic acid. Recently, great progress has been made in the development of biotechnological routes for MA production. This present review provides a comprehensive and systematic overview of recent advances and challenges in biotechnological production of MA. Various biological methods are summarized and compared, and their constraints and possible solutions are also described. Finally, the future prospects are discussed with respect to the current state, challenges, and trends in this field, and the guidelines to develop high-performance microbial cell factories are also proposed for the MA production by systems metabolic engineering. © 2014 Elsevier Inc.


Yan S.,Guangxi Academy of science | Wu G.,DreamSciTech Consulting
Viral Immunology | Year: 2010

The occurrence of swine H1N1 pandemic was unexpected because our previous focus was concentrated on highly pathogenic avian H5N1 outbreaks. The H1N1 pandemic means that cross-species infection and cross-subtype mutation is not as rare as we had previously thought, and the barriers between species and between subtypes are not strong for influenza A virus. In this study, we use ANOVA to determine if there are barriers between species and between subtypes in the matrix protein 1 family from influenza A virus. The results show that the inter-species/subtype variations are generally much smaller than the intra-species/subtype ones, indicating that the barriers between species and between subtypes are not strong for influenza A viruses, which provides statistical evidence for cross-species infections and cro ss-subtype mutations. © 2010 Mary Ann Liebert, Inc.


Yan S.,Guangxi Academy of science | Wu G.,Guangxi Academy of science
Applied Biochemistry and Biotechnology | Year: 2012

This was the continuation of our previous study along the same line with more focus on technical details because the data are usually divided into two datasets, one for model development and the other for model validation during the development of predictive model. The widely used validation method is the delete-1 jackknife validation. However, no systematical studies were conducted to determine whether the jackknife validation with different deletions works better because the number of validations with different deletions increases in a factorial fashion. Therefore it is only small dataset that can be used for such an exhausted study. Cellulase is an enzyme playing an important role in modern industry, and many parameters related to cellulase in enzymatic reactions were poorly documented. With increased interests in cellulases in bio-fuel industry, the prediction of parameters in enzymatic reactions is listed on agenda. In this study, two aims were defined (a) which amino acid property works better to predict the temperature optimum and (b) with which deletion the jackknife validation works. The results showed that the amino acid distribution probability works better in predicting the optimum temperature of catalytic reaction by cellulase, and the delete-4, more precisely one-fifth deletion, jackknife validation works better. © Springer Science+Business Media, LLC 2011.


Yan S.,Guangxi Academy of science | Wu G.,Guangxi Academy of science
Crystal Growth and Design | Year: 2011

Protein crystallization is a process with considerably indescribable difficulties because many known and unknown factors contribute to the process to varying degrees, which are generally defined as amino acid attributes related to physicochemical properties. However, one might wonder whether randomness plays a role in crystallization. Following this, two questions are (i) can we find out the role of randomness in the crystallization process, and (ii) does more randomness or less randomness in a protein make it easily crystallized? In this study, we used logistic regression and neural network with each of 535 amino acid attributes including randomness attributes to fit the successful rate of crystallization of 118 proteins from Plasmodium falciparum; then we developed a predictive model for checking the role of random attributes in predicting protein crystallization, and we compared crystallized proteins with noncrystallized proteins in terms of random amino acid attributes. The results provide three pieces of clear evidence that randomness plays a role in protein crystallization and a protein that has more randomness is more easily crystallized. © 2011 American Chemical Society.


Yan S.,Guangxi Academy of science | Wu G.,Guangxi Academy of science
Applied Biochemistry and Biotechnology | Year: 2011

The optimal working conditions for enzymes are very much elegant, and their determination is often through experimental approach, which generally is costly and time-consuming. Therefore, it is important to develop methods to use as simple as possible information to predict the optimal working condition for enzymes. Cellulase is a very important enzyme widely used in industries. In this study, we attempted to use a 20-1 feedforward backpropagation neural network to screen 24 amino acid properties related to the primary structure of cellulases as predictors to predict the pH optimum in cellulases. The results show that some predictors can predict the pH, especially amino acid distribution probability. © 2011 Springer Science+Business Media, LLC.


Yan S.,Guangxi Academy of science | Wu G.,Guangxi Academy of science
Proteins: Structure, Function and Bioinformatics | Year: 2012

Misgurin is an antimicrobial peptide from the loach, while the hydrophobic-polar (HP) model is a way to study the folding conformations and native states in peptide and protein although several amino acids cannot be classified either hydrophobic or polar. Practically, the HP model requires extremely intensive computations, thus it has yet to be used widely. In this study, we use the two-dimensional HP model to analyze all possible folding conformations and native states of misgurin with conversion of natural amino acids according to the normalized amino acid hydrophobicity index as well as the shortest benchmark HP sequence. The results show that the conversion of misgurin into HP sequence with glycine as hydrophobic amino acid at pH 2 has 1212 folding conformations with the same native state of minimal energy -6; the conversion of glycine as polar amino acid at pH 2 has 13,386 folding conformations with three native states of minimal energy -5; the conversion of glycine as hydrophobic amino acid at pH 7 has 2538 folding conformations with three native states of minimal energy -5; and the conversion of glycine as polar amino acid at pH 7 has 12,852 folding conformations with three native states of minimal energy -4. Those native states can be ranked according to the normalized amino acid hydrophobicity index. The detailed discussions suggest two ways to modify misgurin. © 2012 Wiley Periodicals, Inc.


Yan S.,Guangxi Academy of science | Wu G.,Guangxi Academy of science
Protein and Peptide Letters | Year: 2011

In this study, we attempted to use the neural network to model a quantitative structure-K m (Michaelis-Menten constant) relationship for beta-glucosidase, which is an important enzyme to cut the beta-bond linkage in glucose while K m is a very important parameter in enzymatic reactions. Eight feedforward backpropagation neural networks with different layers and neurons were applied for the development of predictive model, and twenty-five different features of amino acids were chosen as predictors one by one. The results show that the 20-1 feedforward backpropagation neural network can serve as a predictive model while the normalized polarizability index as well as the amino-acid distribution probability can serve as the predictors. This study threw lights on the possibility of predicting the K m in beta-glucosidases based on their amino-acid features. © 2011 Bentham Science Publishers.


Zhuang J.L.,Guangxi Academy of science
Ying yong sheng tai xue bao = The journal of applied ecology / Zhongguo sheng tai xue xue hui, Zhongguo ke xue yuan Shenyang ying yong sheng tai yan jiu suo zhu ban | Year: 2011

In March, June, September, and December 2007, investigations were conducted on the species composition, dominant species, community structure, and abundance distribution of phytoplankton in the Fangchenggang Bay of Guangxi. Based on the investigation data, the phytoplankton abundance, biotic index, and their correlations with environmental factors were analyzed. A total of 138 species of 54 genera were identified, among which, 112 species belonged to 37 genera of diatoms, 21 species belonged to 12 genera of dinoflagellates, 2 species belonged to chrysophyta, 2 species belonged to chlorophyta, and 1 species belonged to cyanophyta. In whole year, the dominant species was Skeletonema costatum. The species number had a trend decreasing from the outer to the inner of the Bay and from spring to winter, while the cell abundance was decreased from the inner to the outer of the Bay. There was an obvious annual change in the cell abundance, being the highest (151.39 x 10(4) cells x dm(-3)) in summer (June) and the lowest (0.35 x 10(4) cells x dm(-3)) in winter (December). In spring, both the diversity and the species number were higher. Correlation analysis demonstrated that the distribution of phytoplankton community had definite correlations with water nutrient content, temperature and salinity. At the observation stations 1 and 2 in west Bay, due to the effects of Fangcheng River runoff and hydrodynamic forces such as tide, water salinity was lower and nutrient content was higher, and accordingly, S. costatum cells in summer could greatly reproduce, even result in high probability of red tide.


Yan S.,Guangxi Academy of science | Wu G.,Guangxi Academy of science
Virulence | Year: 2013

The reassortment of genetic segments from different host species and from different subtypes of influenza A viruses occurs frequently, which may generate new strains causing flu epidemic or pandemic. However, the underlined mechanisms of reassortment were less addressed from the viewpoint of protein variations. Recently, we used the aminoacid pair predictability as an indicator to convert eight types of influenza A virus proteins into predictable portion of amino-acid pairs, and then applied the models I and II ANOVA to estimate their differences in terms of subtypes and host species. In order to get a full picture, 2729 and 1063 non-structural 1 and 2 proteins of influenza A viruses were analyzed in this study. The results are consistent with those obtained from hemagglutinin, neuraminidase, nucleoprotein, polymerase acidic protein, polymerase basic proteins 1 and 2, and matrix proteins 1 and 2, indicating that inter-species/ subtypes variations are smaller than intra-species/subtype ones. Our findings provide statistical evidence that can partially explains why cross-subtype mutation and cross-species infection easily occur during co-infecting of different strains. © 2013 Landes Bioscience.


Yan S.,Guangxi Academy of science | Wu G.,Guangxi Academy of science
Protein and Peptide Letters | Year: 2013

The turnover number is an important parameter to distinguish whether an enzyme is practically workable. Therefore the prediction of turnover number of enzyme will reduce the workload to conduct time-consuming and costly experiments to determine the turnover number. However, no studies have been so far conducted to predict them with respect to cellulose 1,4-beta-cellobiosidase, which is an enzyme used in industries, especially in bio-fuel industry. It is important to develop methods to predict the turnover numbers of cellulose 1,4-beta-cellobiosidases from both wild-type and mutations. In this study, we used neural network models with different amino acid properties, pH levels, temperatures and substrates as inputs to predict the turnover number. The results show that 11 out of 25 amino acid properties analyzed can work as predictor and the amino acid distribution probability is the best one because it can reach smaller mean squared errors during convergence and higher correlation coefficient in two-layer neural network models. This study demonstrates the probability that the neural network model can approximately predict the turnover number of cellulose 1,4-betacellobiosidase. © 2013 Bentham Science Publishers.

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